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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: AI Lifecycle	27%	- AI lifecycle stages: design, training, deployment, monitoring - Model training, inference, and optimization - AI governance, ethics, and compliance - Predictive vs generative AI - Data preparation and management for AI
Topic 2: Security, Reliability, and Operations	15%	- High availability and data protection - Data security and access control for AI - Monitoring, logging, and troubleshooting AI environments - Cost management and efficiency
Topic 3: NetApp AI Solutions and Architecture	25%	- Storage architectures for AI workloads - ONTAP integration with AI frameworks - NetApp AI-ready infrastructure components - Scalability and performance optimization for AI - Data management and data pipeline design
Topic 4: Cloud and Hybrid Cloud AI Deployment	18%	- Cloud-native AI solutions and integration - Hybrid and multi-cloud AI architectures - NetApp cloud data services for AI - Data mobility and consistency across environments
Topic 5: AI Overview	15%	- Algorithm types: supervised, unsupervised, reinforcement learning - AI industry use cases and applications - AI, machine learning, and deep learning concepts - AI deployment models: on-premises, cloud, edge - Convergence of AI, high-performance computing, and analytics

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q90-Q95):

NEW QUESTION # 90
Which of the following are typical requirements for inferencing in AI models? (Choose two)

A. Pre-trained models
B. Loading the model into memory
C. Cloud-based inference only
D. Extensive training data

Answer: A,B

NEW QUESTION # 91
An internal audit requires the firm to prove the exact state of the financial product documentation that was used by the chatbot to answer a specific query from last Tuesday. The query log shows the request was processed at '2025-07-11T14:30:00Z'. The data pipeline that updates the vector database runs daily at midnight.
Which NetApp technology allows the team to instantly access an immutable, point-in-time version of the vector database volume as it existed on that specific day?

A. NetApp FlexClone
B. NetApp SnapMirror
C. NetApp FabricPool
D. NetApp Snapshots

Answer: D

NEW QUESTION # 92
A financial services company is required by regulators to be able to trace any version of their deployed fraud detection model back to the exact dataset and source code commit used to train it.
The current MLOps workflow is as follows:
Code_Repository: Git (commit hash: a1b2c3d4)
Dataset_Location: /vol/prod_data/fraud_dataset_v3
Storage_System: NetApp ONTAP 9
Model_Output: /vol/models/fraud_model_v3.2
Which NetApp technology should be used to create an immutable, point-in-time, and space- efficient copy of the dataset that can be linked to the specific code commit and model version?

A. NetApp FlexClone
B. NetApp SnapMirror
C. NetApp FabricPool
D. NetApp Snapshots

Answer: D

NEW QUESTION # 93
Which of the following platforms provides tools for model training and deployment specifically for AI workloads?

A. Domino Data Labs
B. Google VertexAI
C. RunAI
D. All of the above

Answer: D

NEW QUESTION # 94
Which storage protocols are commonly used for handling large-scale AI data? (Choose two)

A. POSIX-based file systems
B. File-based systems
C. Parallel file systems
D. Object-based storage

Answer: C,D

NEW QUESTION # 95
......

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